Validation of a Model of Extramusical Influences on Solo and Small-Ensemble Festival Ratings

Abstract

This is the fourth in a series of studies whose purpose has been to develop a theoretical model of selected extramusical variables' ability to explain solo and small-ensemble festival ratings. Authors of the second and third of these (Bergee & McWhirter, 2005; Bergee & Westfall, 2005) used logistic regression as the basis for their model-building strategy. Modeling in these two studies strongly converged, demonstrating that performing as a soloist later in the day and entering from a large, metropolitan-area, relatively well-financed school were success indicators. The present study constitutes the validation phase of this series. First, the 2004 ratings data from a large Midwestern state's solo and small ensemble music festival were analyzed using binomial logistic regression. Model variables retained to this point in the process-time of day (morning/afternoon), geographical district (metropolitan/nonmetropolitan), district expenditure per average daily attendance (defined with two categorical variables), school size classification (highest/four lowest classifications), type of event (sololensemble), and the time of day by geographical district interaction—were regressed on a dichotomized ("I" vs. "not I" ) festival ratings variable. The model then underwent external (cross) validation by means of its application to the 2002 and 2003 festival data. Although some shrinkage was noted, results indicated an acceptable fit with these two data sets. For internal validation, 50 random samples of 25 % of the 2004 entrants were drawn and submitted to binomial logistic regression analysis. Then, 50 random samples of 10 % were drawn and likewise analyzed. Results indicated that estimated coefficients showed unbiasedness and consistency. On the other hand, a marked inefficiency among the estimates was found. The model also showed evidence of under specificity.

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References

Adderly, C.L. (2001). Does the hour of the day affect student selection for instrumental honors ensembles? Contributions to Music Education, 28, 103-113.

Bergee, M.J. , & Platt, M.C. (2003). Influence of selected variables on solo and ensemble festival ratings. Journal of Research in Music Education, 51, 342-353.

Bergee, M.J. , & McWhirter, J.L. (2005). Selected influences on solo and ensemble festival ratings: Replication and extension. Journal of Research in Music Education, 53, 177-190.

Bergee, M.J. , & Westfall, C.R. (2005). Stability of a model explaining selected extramusical influences on solo and small-ensemble festival ratings . Journal of Research in Music Education, 53, 253-271.

Berry, W.D. , & Feldman, S. (1985). Multiple regression in practice. Sage University Papers Series on Quantitative Applications in the Social Sciences 07-050. Thousand Oaks, CA: Sage.

Bruine de Bruin, W. (2005). Save the last dance for me: Unwanted serial position effects in jury evaluations. Acta Psychologica, 118, 245-260.

Cohen, J. (1994). The earth is round (P <.05). American Psychologist , 49, 997-1003.

Elliott, C.A. , Schneider, M.C. , & Zembower, C.M. (2000). Influence of the audition hour on selection to an all-state band. Journal of Band Research , 35 (2), 20-31.

Flôres, R.G. , & Ginsburgh, V.A. (1996). The Queen Elisabeth musical competition : How fair is the final ranking? The Statistician , 45, 97-104.

10.

Ginsburgh, V.A. , & van Ours, J.C. (2003) . Expert opinion and compensation: Evidence from a musical competition. American Economic Review , 93, 289-296.

11.

Glejser, H. , & Heyndels, B. (2001). Efficiency and inefficiency in the ranking of competitions: The case of the Queen Elisabeth Music Contest. journal of Cultural Economics, 25, 109-129.

12.

Hogarth, R.M. , & Einhorn, H.J. (1992). Order effects in belief updating: The belief adjustment model. Cognitive Psychology, 24, 1-55.

13.

Hosmer, D.W. , & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). New York: John Wiley & Sons.

14.

Houston, D.A. , Sherman, S.J. , & Baker, S.M. (1989). The influence of unique features and direction of comparison on preferences. Journal of Experimental Social Psychology, 25, 121-141.

15.

McPherson, G.E. , & Schubert, E. (2004). Measuring performance enhancement in music. In A. Williamson (Ed.), Musical excellence: Strategies and techniques to enhance performance (pp. 60-82). Oxford , UK: Oxford University Press.

16.

Moore, D.A. (1999). Order effects in preference judgments: Evidence for context dependence in the generation of preferences. Organizational Behavior and Human Decision Processes, 78, 146-165.

17.

Nunnally, J.C. , & Bernstein, I.H. (1994). Psychometric theory. New York: McGraw-Hill.

18.

Stevens, J. (1996). Applied multivariate statistics for the social sciences (3rd ed.). Mahwah, NJ : Erlbaum.

19.

Wapnick, J. , Flowers, P. , Alegant, M. , & Jasinskas, L. (1993). Consistency in piano performance evaluation. Journal of Research in Music Education, 41, 282-292.

20.

Weekley, J.A. , & Gier, J.A. (1989). Ceilings in the reliability and validity of performance ratings: The case of expert raters. Academy of Management Journal, 32, 213-222.

21.

Wilson, V.E. ( 1977) . Objectivity and effect of order of appearance in judging of synchronized swimming meets. Perceptual and Motor Skills, 44, 295-298.

22.

Wright, R.E. (1995). Logistic regression. In L. G. Grimm & P. R. Yarnold (Eds.), Reading and understanding multivariate statistics (pp. 217-244). Washington, DC: APA.